Paper
15 July 2004 Texture synthesis based on transition probabilities and localized correlation
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Abstract
This paper introduces an approach for synthesizing natural textures, with emphasis on quasi-periodic and structural textures. The process consists of two stages. In the first stage, the basic textural elements (texels) and the basic textural structure are determined. This is achieved by identifying two fundamental frequencies in the texture, for two different orientations. The basic structure is a non-regular mesh that defines the place holders for texels. We call such place holders e-texels (empty texels). In the second stage, a new textural structure is designed from the original one, and its e-texels are filled in by texels obtained from the original patch. Same texture texels are expected to possess a high degree of similarity, thus the new structure could be filled in at random. However, a transition probability approach is used in order to retain local textural characteristics. More specifically, assuming that texel A is the last texel placed in the new structure, the e-texel closest to A is found. The e-texel is replaced by texel B from the old structure if the relative position between A and the e-texel is similar to the relative position between A and B in the old structure. This technique is an extension of a general texture synthesis technique previously developed by the author. The proposed technique is suited for structural textures since blockage effects are eliminated by allowing irregular shape texels to be merged, contrary to the previous general technique where the blocks merged are squares. Results show that the proposed method is successful in synthesizing structural textures.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dimitrios Charalampidis "Texture synthesis based on transition probabilities and localized correlation", Proc. SPIE 5438, Visual Information Processing XIII, (15 July 2004); https://doi.org/10.1117/12.546164
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KEYWORDS
Fourier transforms

Structural design

Machine vision

Visualization

Computer graphics

Electrical engineering

Image classification

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